Research Topics:

Data Analysis | Statistical Analysis

Content Type:

Glossary

Correspondence Analysis Definition

A method of factoring categorical variables and displaying them in a property space which maps their association in two or more dimensions. It is often used where a tabular approach is less effective due to large tables with many rows and/or columns.

Correspondence analysis explores relationships and patterns within categorical data. This statistical technique identifies associations among categorical variables, thus allowing them to be displayed in a property space that maps their association in two or more dimensions. In other words, it converts complex data into a simpler format that can be easily interpreted and analyzed. It often is used when a tabular approach is less effective because of large tables with many rows and/or columns. This analysis is valuable to marketing professionals because it illustrates the interplay between different categorical variables and reveals connections that impact consumer behavior and preferences. Ultimately, it distills complex data into actionable insights.

Who relies on correspondence analysis?

Marketing researchers and analysts, as well  as data scientists, use correspondence analysis when dealing with large sets of categorical data, like survey responses, customer preferences, product features and demographic information. In addition, businesses seeking insights into consumer behavior, market segmentation and brand positioning use insights from the technique to make decisions.

Why should I care about correspondence analysis?

Correspondence analysis uncovers hidden patterns in data that highlight associations that might not be visible through other analysis. Visual representations of relationships between variables helps marketing professionals to make data-driven decisions, optimize marketing strategies and identify target audiences.